Evaluation metrics. We start by evaluating the performance of our method and existing approaches [1]}, [2]} at detecting outliers in LiDAR depth maps. Purposely, we compute the Area Under the sparsification Curve (AUC), a standard metric for this task [3]}, [4]}, [5]}. Namely, for each depth map in the dataset, pixels with both LiDAR and ground-truth depth available are sorted in increasing order of confidence score and gradually removed (2% each time). The Root Mean Squared Error (RMSE) over remaining pixels is computed each time and a curve is drawn. The area under the curve quantitatively assesses the effectiveness at removing outliers (the lower, the better). Optimal AUC is obtained by removing pixels in decreasing order of depth error.
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